Monocytes and Macrophages in Kidney Transplantation and Insights from Single Cell RNA-Seq Studies

And Function

Single-cell RNA sequencing (scRNA-seq) is a powerful technology that allows for the identification of minority cell types in complex tissues, such as immune cells in the kidney. Previously, gene expression from infrequent cell types was missed using bulk RNA-sequencing methods due to an averaging effect. Additionally, single-cell RNA sequencing facilitates assignment of cell origin in a sample, a shortcoming of previous bulk sequencing technologies. Thus, scRNA-seq is ideal to study the immune cell landscape and the alloimmune response in the human kidney transplant. However, there are few studies published to date. Macrophages are known to play an important role in health and disease in the kidney. Furthermore, it is known that macrophages play key roles in rejection of the kidney transplant. The definition, ontogeny, and function of these cells is complex and nomenclature has evolved as new technologies have become available. In this review, an overview of monocyte and macrophage nomenclature, ontogeny, and function with a specific focus on kidney transplantation is provided with novel scRNA-seq findings included. Single-cell RNA sequencing offers an unbiased transcriptional approach to defining macrophages and provides insights into macrophage ontogeny and function not possible with contemporary methods.

Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant

Journal of the American Society of Nephrology ◽

10.1681/asn.2020030326 ◽

2020 ◽

Vol 31 (9) ◽

pp. 1977-1986 ◽

Cited By ~ 2

Author(s):

Andrew F. Malone ◽

Haojia Wu ◽

Catrina Fronick ◽

Robert Fulton ◽

Joseph P. Gaut ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

Rna Sequencing ◽

Kidney Transplant ◽

Immune Cell ◽

Solid Organ ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Transcriptional Profiles ◽

BackgroundIn solid organ transplantation, donor-derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient from donor cells.MethodsWhole-exome sequencing of donor and recipient DNA and single-cell RNA sequencing (scRNA-seq) of five human kidney transplant biopsy cores distinguished immune cell contributions from both participants. DNA-sequence comparisons used single nucleotide variants (SNVs) identified in the exome sequences across all samples.ResultsAnalysis of expressed SNVs in the scRNA-seq data set distinguished recipient versus donor origin for all 81,139 cells examined. The leukocyte donor/recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages displayed inflammatory activation whereas donor macrophages demonstrated antigen presentation and complement signaling. Recipient-origin T cells expressed cytotoxic and proinflammatory genes consistent with an effector cell phenotype, whereas donor-origin T cells appeared quiescent, expressing oxidative phosphorylation genes. Finally, both donor and recipient T cell clones within the rejecting kidney suggested lymphoid aggregation. The results indicate that donor-origin macrophages and T cells have distinct transcriptional profiles compared with their recipient counterparts, and that donor macrophages can persist for years post-transplantation.ConclusionsAnalysis of single nucleotide variants and their expression in single cells provides a powerful novel approach to accurately define leukocyte chimerism in a complex organ such as a transplanted kidney, coupled with the ability to examine transcriptional profiles at single-cell resolution.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/JASN/2020_08_07_JASN2020030326.mp3

ACTINN: automated identification of cell types in single cell RNA sequencing

Bioinformatics ◽

10.1093/bioinformatics/btz592 ◽

2019 ◽

Cited By ~ 7

Author(s):

Feiyang Ma ◽

Matteo Pellegrini

Keyword(s):

Neural Network ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Types ◽

Mouse Cell ◽

Supplementary Information ◽

Cell Type ◽

Human T Cell ◽

Abstract Motivation Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. Results We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Availability and implementation The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. Supplementary information Supplementary data are available at Bioinformatics online.

037 Functional interrogation of immune cell types identified by single-cell RNA sequencing in alopecia areata

Journal of Investigative Dermatology ◽

10.1016/j.jid.2021.02.053 ◽

2021 ◽

Vol 141 (5) ◽

pp. S7

Author(s):

E.Y. Lee ◽

Z. Dai ◽

E.H. Wang ◽

E. Chang ◽

A.M. Christiano

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Alopecia Areata ◽

Immune Cell ◽

Cell Types ◽

Kidney pathological changes alter cell clustering features in single-cell RNA sequencing

10.1101/2021.10.08.463030 ◽

2021 ◽

Author(s):

Lijun Ma ◽

Mariana Murea ◽

Young A Choi ◽

Ashok K. Hemal ◽

Alexei V. Mikhailov ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Kidney Tissue ◽

Human Kidney ◽

Cell Types ◽

Specific Gene ◽

Glomerular Injury ◽

Cell Clustering ◽

The kidney is composed of multiple cell types, each with specific physiological functions. Single-cell RNA sequencing (scRNA-Seq) is useful for classifying cell-specific gene expression profiles in kidney tissue. Because viable cells are critical in scRNA-Seq analyses, we report an optimized cell dissociation process and the necessity for histological screening of human kidney sections prior to performing scRNA-Seq. We show that glomerular injury can result in loss of select cell types during the cell clustering process. Subsequent fluorescence microscopy confirmed reductions in cell-specific markers among the injured cells seen on kidney sections and these changes need to be considered when interpreting results of scRNA-Seq.

Single-Cell Transcriptomics of a Human Kidney Allograft Biopsy Specimen Defines a Diverse Inflammatory Response

Journal of the American Society of Nephrology ◽

10.1681/asn.2018020125 ◽

2018 ◽

Vol 29 (8) ◽

pp. 2069-2080 ◽

Cited By ~ 90

Author(s):

Haojia Wu ◽

Andrew F. Malone ◽

Erinn L. Donnelly ◽

Yuhei Kirita ◽

Kohei Uchimura ◽

...

Keyword(s):

Endothelial Cell ◽

Single Cell ◽

Rna Sequencing ◽

Biopsy Specimen ◽

Kidney Biopsy ◽

Human Kidney ◽

Cell Types ◽

Biopsy Specimens ◽

Transplant Biopsy

Background Single-cell genomics techniques are revolutionizing our ability to characterize complex tissues. By contrast, the techniques used to analyze renal biopsy specimens have changed little over several decades. We tested the hypothesis that single-cell RNA-sequencing can comprehensively describe cell types and states in a human kidney biopsy specimen.Methods We generated 8746 single-cell transcriptomes from a healthy adult kidney and a single kidney transplant biopsy core by single-cell RNA-sequencing. Unsupervised clustering analysis of the biopsy specimen was performed to identify 16 distinct cell types, including all of the major immune cell types and most native kidney cell types, in this biopsy specimen, for which the histologic read was mixed rejection.Results Monocytes formed two subclusters representing a nonclassical CD16+ group and a classic CD16− group expressing dendritic cell maturation markers. The presence of both monocyte cell subtypes was validated by staining of independent transplant biopsy specimens. Comparison of healthy kidney epithelial transcriptomes with biopsy specimen counterparts identified novel segment-specific proinflammatory responses in rejection. Endothelial cells formed three distinct subclusters: resting cells and two activated endothelial cell groups. One activated endothelial cell group expressed Fc receptor pathway activation and Ig internalization genes, consistent with the pathologic diagnosis of antibody-mediated rejection. We mapped previously defined genes that associate with rejection outcomes to single cell types and generated a searchable online gene expression database.Conclusions We present the first step toward incorporation of single-cell transcriptomics into kidney biopsy specimen interpretation, describe a heterogeneous immune response in mixed rejection, and provide a searchable resource for the scientific community.

Application of single-cell RNA sequencing methodologies in understanding haematopoiesis and immunology

Essays in Biochemistry ◽

10.1042/ebc20180072 ◽

2019 ◽

Vol 63 (2) ◽

pp. 217-225 ◽

Cited By ~ 2

Author(s):

Anna M. Ranzoni ◽

Paulina M. Strzelecka ◽

Ana Cvejic

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Types ◽

Tree Model ◽

Future Directions ◽

Lineage Tree ◽

Cell Variation ◽

Cellular Components

Abstract The blood and immune system are characterised by utmost diversity in its cellular components. This heterogeneity can solely be resolved with the application of single-cell technologies that enable precise examination of cell-to-cell variation. Single-cell transcriptomics is continuously pushing forward our understanding of processes driving haematopoiesis and immune responses in physiological settings as well as in disease. Remarkably, in the last five years, a number of studies involving single-cell RNA sequencing (scRNA-seq) allowed the discovery of new immune cell types and revealed that haematopoiesis is a continuous rather than a stepwise process, thus challenging the classical haematopoietic lineage tree model. This review summarises the most recent studies which applied scRNA-seq to answer outstanding questions in the fields of haematology and immunology and discusses the present challenges and future directions.

Case Report: Transformation From Cold to Hot Tumor in a Case of NSCLC Neoadjuvant Immunochemotherapy Pseudoprogression

Frontiers in Immunology ◽

10.3389/fimmu.2021.633534 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wenxiao Jia ◽

Hui Zhu ◽

Qianqian Gao ◽

Jian Sun ◽

Fujian Tan ◽

...

Keyword(s):

Lymph Node ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Types ◽

Surgical Pathology ◽

Mass Cytometry ◽

Squamous Cancer ◽

Mediastinum Lymph Node

A 56-year-old male was diagnosed with right lung upper lobe squamous cancer with right hilar and mediastinum lymph node metastasis. After four cycles of neoadjuvant immunochemotherapy, reexamination by computed tomography showed progressive disease of the primary lesion. Then, the patient underwent a right lung upper lobectomy, and hilar and mediastinum lymph node dissection. Surgical pathology showed a partial response to immunochemotherapy. Single-cell RNA sequencing was used to characterize the infiltrating immune cell atlas after neoadjuvant immunochemotherapy; the most common infiltrating immune cell types were cytotoxic CD8+ T cells, monocyte-derived dendritic cells, and macrophages. Imaging mass cytometry revealed a transformation from cold to hot tumor after neoadjuvant immunochemotherapy. In this case study, we are the first to report a case of neoadjuvant immunochemotherapy pseudoprogression, proved by surgical pathology, single-cell RNA sequencing, and imaging mass cytometry. Both single-cell RNA sequencing and imaging mass cytometry revealed an activated immune microenvironment after neoadjuvant immunochemotherapy.

Automated identification of Cell Types in Single Cell RNA Sequencing

10.1101/532093 ◽

2019 ◽

Cited By ~ 3

Author(s):

Feiyang Ma ◽

Matteo Pellegrini

Keyword(s):

Neural Network ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Types ◽

Marker Genes ◽

Complex Data ◽

Cell Type ◽

Human T Cell ◽

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

A molecular cell atlas of the human lung from single cell RNA sequencing

10.1101/742320 ◽

2019 ◽

Cited By ~ 39

Author(s):

Kyle J. Travaglini ◽

Ahmad N. Nabhan ◽

Lolita Penland ◽

Rahul Sinha ◽

Astrid Gillich ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Human Lung ◽

Expression Profiles ◽

Cell Types ◽

Specific Gene ◽

Cell Trafficking ◽

AbstractAlthough single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to ∼75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.